1. Technical Field
This disclosure relates to security systems for protecting military and other perimeters from an approaching human and vehicle and to neural networks.
2. Description of Related Art
Perimeter protection system may be crucial to the protection of military or other assets. Detecting threats prior to the intrusion may be the first step. However, some types of detection sensors may be easily detected and disabled by intruders.
Geophones may be less conspicuous and thus less subject to disruption. The centralized processing of signals from a series of geophones has been used for detecting security breaches. See Pakhomov, A. Sicignano, M. Sandy, and T. Goldburt, “A Novel Method for Footstep Detection with an Extremely Low False Alarm Rate,” in Proc. SPIE Symposium on Unattended Ground Sensor Technologies and Applications V, 2003, SPIE vol. 5090, pp. 186-193. Detecting human footsteps vs. other types of background vibration such as those caused by vehicles has also been suggested. See G. Succi, D. Clapp, and R. Gambert, “Footstep, Detection and Tracking,” in Proc. of the SPIE, Unattended Ground Sensor Technologies and Applications III, 2001, SPIE vol. 4393, pp. 22-29. Kurtosis and cadence have been measured to detect footsteps. Id. Extracting cadence features using a spectrum analysis technique has also been suggested. See K. M. Houston and D. P. McGaffigan, “Spectrum Analysis Techniques for Personnel Detection using Seismic Sensors,” in Proc. SPIE Symposium on Unattended Ground Sensor Technologies and Applications V, 2003, SPIE vol. 5090, pp. 162-173. Kurtosis has been employed to detect the event, and detection results were confirmed with cepstrum analysis to improve the results. See L. Peck and J. Lacombe, “Seismic-based personnel detection,” in Proc. 41st Annual IEEE International Carnahan Conference on Security Technology, 2007, pp. 169-175.
Kurtosis may distinguish impulsive and rhythmic events from sustained background vibrations. However, using Kurtosis may result in confusion between footstep and similar impulsive events. The measurement of cadence may require a high signal to noise ratio. To achieve this, events may need to occur close to the sensor, which may reduce detection range. In addition, kurtosis and cadence may only be effective for footstep recognition. They may be unable to recognize vibration caused by a moving vehicle.
The performance of dynamic synapse neural networks (“DSNN”) has been compared to other pattern recognition algorithms. See J-S. Liaw, and T. W. Berger, “The dynamic synapse: A new concept for neural representation and computation,” Hippocampus, vol. 6, pp 591-600, 1996. DSNN has also been applied to the detection of a footstep and vehicle. See A. A. Dibazar, H. O. Park, and T. W. Berger, “The Application of Dynamic Synapse Neural Networks on Footstep and Vehicle Recognition,” in Proc. IEEE International Joint Conference on Neural Networks, 2007, pp. 1842-1846.
None of these approaches, however, may be able to satisfactorily differentiate between an approaching human or vehicle, on the one hand, and quadrupedal animal footsteps or background or other noise on the other hand.